The chapter “Putting It Together: Fitting a Dynamic Model” provides a synthesis of the material presented in the book, by presenting a worked example that embraces concepts of density dependence, complex model dynamics, parameter estimation, and model selection using the Akaike information criterion. The example chosen is the recovery of gray wolf (Canis lupus) population in Washington State since 2008. The chapter begins by explaining how to fit an observation error model. Next, it examines how to fit a process error model. It then discusses parameter estimates and model selection. The chapter concludes with discussion of how to determine whether the population given as an example exhibits complex population dynamics.
A weakly informative prior for Bayesian dynamic model selection with applications in fMRI
